Libraries, and More Libraries.. Soon

ML.NET contains machine learning libraries created by Microsoft Research and used by Microsoft products. Over time, you will also be able to leverage other popular libraries like Accord.NET, CNTK and TensorFlow through the extensible platform.

How to get this technology to work for you?

What Data Can you Load?

ML.NET can load the following types of data into your pipeline:

Text (CSV/TSV)

Parquet

Binary

IEnumerable<Τ>

File sets

Data in the Format You Need

Use the built-in set of transforms to get your data into the format and types that you need for processing. ML.NET offers support for:

Text transforms

Changing data schema

Handling missing data values

Categorical variable encoding

Normalization

Selecting relevant training features

NGram featurization

Choose Algorithm to Meet your Needs

Choose the learning algorithm that will provide the highest accuracy for your scenario. ML.NET offers the following types of learners:

Linear (e.g. SymSGD, SDCA)

Boosted Trees (e.g. FastTree, LightGBM)

K-Means

SVM

Averaged Perceptron

Many Models to Choose

Train Model: Training your model by calling the LearningPipeline.Train method. The method will then return a PredictionModel object that uses your input and output types to make predictions.

Evaluate Model: ML.NET offers evaluators that will assess the performance of your model on a variety of metrics.

Deploy Model: ML.NET allows you to save your trained model as a binary file that you can integrate into any .NET application.